Radio Frequency Identification Signal Research Articles

Applying RFID and NLP for efficient warehouse picking

This paper proposes an intelligent warehouse-picking approach using radio frequency identification (RFID) indoor positioning and natural language processing (NLP) speech recognition. A forward maximum matching algorithm segments speech into domain terminology. Location was estimated by RFID signal strengths between reference tags and pickers. Simulation results demonstrated a 50% reduction in segmentation runtime versus conventional methods. Speech recognition accuracy reached 90–95%, improving by 23% over baseline. Positioning accuracy also increased substantially. The techniques can reduce picking errors and costs. Further work should evaluate performance in real-world environments.

Drone Detection and Tracking Using RF Identification Signals.

The market for unmanned aerial systems (UASs) has grown considerably worldwide, but their ability to transmit sensitive information poses a threat to public safety. To counter these threats, authorities, and anti-drone organizations are ensuring that UASs comply with regulations, focusing on strategies to mitigate the risks associated with malicious drones. This study presents a technique for detecting drone models using identification (ID) tags in radio frequency (RF) signals, enabling the extraction of real-time telemetry data through the decoding of Drone ID packets. The system, implemented with a development board, facilitates efficient drone tracking. The results of a measurement campaign performance evaluation include maximum detection distances of 1.3 km for the Mavic Air, 1.5 km for the Mavic 3, and 3.7 km for the Mavic 2 Pro. The system accurately estimates a drone's 2D position, altitude, and speed in real time. Thanks to the decoding of telemetry packets, the system demonstrates promising accuracy, with worst-case distances between estimated and actual drone positions of 35 m for the Mavic 2 Pro, 17 m for the Mavic Air, and 15 m for the Mavic 3. In addition, there is a relative error of 14% for altitude measurements and 7% for speed measurements. The reaction times calculated to secure a vulnerable site within a 200 m radius are 1.83 min (Mavic Air), 1.03 min (Mavic 3), and 2.92 min (Mavic 2 Pro). This system is proving effective in addressing emerging concerns about drone-related threats, helping to improve public safety and security.

Monitoring snow water equivalent using the phase of RFID signals

Abstract. The amount of water contained in a snowpack, known as snow water equivalent (SWE), is used to anticipate the amount of snowmelt that could supply hydroelectric power plants, fill water reservoirs, or sometimes cause flooding. This work introduces a wireless, non-destructive method for monitoring the SWE of a dry snowpack. The system is based on an array of low-cost passive radiofrequency identification (RFID) tags, placed under the snow and read at 865–868 MHz by a reader located above the snow. The SWE was deduced from the phase delay of the tag's backscattered response, which increases with the amount of snow traversed by the radiofrequency wave. Measurements taken in the laboratory, during snowfall events and over 4.5 months at the Col de Porte test field, were consistent with reference measurements of cosmic rays, precipitation and snow pits. SWE accuracy was ±18 kg m−2 throughout the season (averaged over three tags) and ±3 kg m−2 during dry snowfall events (averaged over data from two antennas and four or five tags). The overall uncertainty compared to snow weighing was ±10 % for snow density in the range 61–390 kg m−3. The main limitations observed were measurement bias caused by wet snow (biased data were discarded) and the need for phase unwrapping. The method has a number of advantages: it allows for continuous measurement (1 min sampling rate in dry snow), it can provide complementary measurement of tag temperature, it does not require the reception of external data, and it opens the way towards spatialized measurements. The results presented also demonstrate that RFID propagation-based sensing can remotely monitor the permittivity of a low-loss dielectric material with scientific-level accuracy.

Dielectric Waveguide Filled With Particulate Media for Ultrahigh Frequency (UHF) Radio Frequency Identification (RFID) Applications

Radio frequency identification (RFID) is a wireless communication technique that has a wide variety of applications in many fields. In some cases, the read range of the RFID transponder is a limiting factor to the application; thus, it is desired that the read range of a given RFID system be maximized. This paper presents a method to extend the read range of UHF RFID signal (860-960 MHz) by using cost-effective dielectric particulate materials (e.g., dry sand) as filler in dielectric waveguides, which is useful for applications that employ UHF RFID as a sensing or communication tool that need long read ranges, e.g., underground pipeline corrosion monitoring or leak detection. Reading tests conducted in both laboratory (air) and underground showed that the transmission of the UHF RFID signal through dielectric particulate media can considerably extend the read range of passive RFID transponders, which makes it a good material choice for the manufacturing of wireless monitoring systems for underground infrastructures. Classic waveguide theories developed by Marcatili and Emslie et al. were modified to analyze the transmission of the UHF RFID signal in the waveguide filled with dielectric particulate media. Reasonably good agreement was observed between the measured and calculated power values.

Wear-free gesture recognition based on residual features of RFID signals

Traditionally, RFID is frequently used in identification and localization. In this paper, an extension application of RFID is designed to recognize gestures. Currently, gesture recognition is mainly used for feature extraction through wearable sensors and video cameras, which have shortcomings such as inconvenience to carry and interference with obstacles. This paper proposes a gesture recognition system based on radio frequency identification (RFID), where users do not need to wear devices. In the proposed model, the interference information generated by the gesture action on the tag signal is used as the fingerprint feature of the action. To obtain satisfactory recognition, the signal diversity is first increased through the tag array. Then, the RSSI and phase signal are normalized to eliminate offset and noise before training. Furthermore, a residual neural network (ResNet) is carefully built as a gesture classification model. The experimental results show that the recognition system achieves more recognition accuracy than existing methods, and the average gesture recognition accuracy reaches 95.5%.

A Batteryless RFID Sensor Architecture With Distance Ambiguity Resolution for Smart Home IoT Applications

In this article, a novel batteryless wireless sensor design is proposed and demonstrated. The proposed passive wireless sensor node integrates a ultrahigh-frequency (UHF) radio-frequency identification (RFID) chip, a filter, and a reactive sensor to enable the reading of environmental conditions wirelessly, without increasing the complexity of a typical RFID chip and substantial loss of the read range. The sensor changes the phase of the reflected RFID signal based on the sensed parameter; whereas, the filter helps in resolving any phase ambiguity that may arise due to the placement of the node at different distances. The phase change is detected at the reader using a noncoherent IQ demodulator. The design can easily integrate any type of sensors, such as temperature, humidity, and water-level sensor. A flood sensor and a temperature sensor were used to demonstrate the performance of the proposed design in a home and office environment.

Monitoring coastal aquaculture devices in Taiwan with the radio frequency identification combination system

ABSTRACT Marine debris significantly influences the environment and economics of marine ecosystems. Measures to ban illegally discarded materials using monitoring techniques are expected to mitigate marine debris mismanagement. For example, derelict oyster farming rafts along the southwestern coast of Taiwan have been a source of marine debris pollution since the 1980s. An efficient inspection system is an urgent requirement for monitoring oyster farming areas and, is expected to implement control and surveillance measures for litter discarded from coastal fisheries. To address this issue, this study examined combinations of radio frequency identification (RFID), drone archiving, and onshore receiving systems to ascertain the positions of oyster rafts and their owners with digitally tagged labels. The results showed that the two proposed monitoring systems are feasible for use in marine environments. The RFID signals archived by the drone reached 100% of the 200 tags with a spatial bias ranging from 2.38 to 59.99 m. RFID-GPS hybrid tag signals received by the onshore station covered 100% of the 20 tags with spatial bias ranging from 1.27 to 10.47 m. The RFID-GPS hybrid system was confirmed as a feasible approach for monitoring oyster rafts within 3 km of the coast. The real-time (1 h intervals) position and attributes of each raft detected by the system indicated that our designed techniques enhance responsible fishery surveillance and management of coastal aquaculture worldwide.

Waymarking in Social Robots: Environment Signaling Using Human-Robot Interaction.

Travellers use the term waymarking to define the action of posting signs, or waymarks, along a route. These marks are intended to be points of reference during navigation for the environment. In this research, we will define waymarking as the skill of a robot to signal the environment or generate information to facilitate localization and navigation, both for its own use and for other robots as well. We present an automated environment signaling system using human–robot interaction and radio frequency identification (RFID) technology. The goal is for the robot, through human–robot interaction, to obtain information from the environment and use this information to carry out the signaling or waymarking process. HRI will play a key role in the signaling process since this type of communication makes it possible to exchange more specific and enriching information. The robot uses common phrases such as “Where am I?” and “Where can I go?”, just as we humans do when we ask other people for information about the environment. It is also possible to guide the robot and “show” it the environment to carry out the task of writing the signs. The robot will use the information received to create, update, or improve the navigation data in the RFID signals. In this paper, the signaling process will be described, how the robot acquires the information for signals, writing and updating process and finally, the implementation and integration in a real social robot in a real indoor environment.

Localization and Tracking of an Indoor Autonomous Vehicle Based on the Phase Difference of Passive UHF RFID Signals.

State-of-the-art radio frequency identification (RFID)-based indoor autonomous vehicles localization methods are mostly based on received signal strength indicator (RSSI) measurements. However, the accuracy of these methods is not high enough for real-world scenarios. To overcome this problem, a novel dual-frequency phase difference of arrival (PDOA) ranging-based indoor autonomous vehicle localization and tracking scheme was developed. Firstly, the method gets the distance between the RFID reader and the tag by dual-frequency PDOA ranging. Then, a maximum likelihood estimation and semi-definite programming (SDP)-based localization algorithm is utilized to calculate the position of the autonomous vehicles, which can mitigate the multipath ranging error and obtain a more accurate positioning result. Finally, vehicle traveling information and the position achieved by RFID localization are fused with a Kalman filter (KF). The proposed method can work in a low-density tag deployment environment. Simulation experiment results showed that the proposed vehicle localization and tracking method achieves centimeter-level mean tracking accuracy.

Influences of exposure to 915-MHz radiofrequency identification signals on serotonin metabolites in rats: a pilot study

Purpose The influences of radiofrequency electromagnetic exposure on animal health, particularly on serotonin metabolism, are not well-elucidated. In this in vivo study, we studied the influences of exposure to radiofrequency identification (RFID) signals on serotonin metabolism. Materials and methods Twenty-two male Sprague–Dawley rats were assigned to sham (n = 10) and RFID-exposed (n = 12) groups. Rats in the RFID-exposed group were exposed to RFID signals at an average whole-body specific absorption rate of 2 W/kg for 8 h/day, 5 days/week for 2 weeks. Before and after RFID exposure, 24-h urine was collected from each rat. Urinary tryptophan, 5-hydroxytryptophan, serotonin, 5-hydroxyindoleacetic acid, and 5-methoxyindole-3-acetic acid concentrations were examined using gas chromatography-mass spectrometry, and changes in the patterns of values were compared between the two groups. Results Urinary levels of serotonin decreased by 20% (p = .041, Student’s t-test) and 40% (p = .024, Student’s t-test) in both the sham and RFID-exposed groups, respectively. The level of 5-methoxyindole-3-acetic acid decreased by 30% in the RFID-exposed group (p = .039, Student’s t-test). Conclusion Our results indicate that exposure to RFID signals at a specific absorption rate of 2 W/kg is sufficient to alter serotonin metabolism in rats regardless of whether the exposure level is considered biohazardous.

Emotion monitoring with RFID: an experimental study

Emotion recognition can be helpful in many fields such as elderly healthcare. Existing emotion recognition approaches are usually based on wearable sensors or computer vision analysis, which are intrusive or inconvenient to use. In recent years, radio frequency identification (RFID) has been exploited to monitor physiological signs (e.g., respiration and heartbeat) of users in a contactless and convenient way. Motivated by such progresses, we conduct an experimental study on recognizing the emotion of users with commercial RFID devices. We propose Free-EQ, an emotion recognition framework which first extracts respiration-based features and heartbeat-based features from RFID signals and then uses these features to train a classifier to recognize different emotions of a target user. Experiments on commercial RFID hardware show that Free-EQ can distinguish different emotions with relatively high accuracy.

An algorithm for automatic grasping an UHF RFID passive tag

This paper proposes an automatic system which allows a mobile robot to reach a tagged object in an unknown environment, exploiting the Radio Frequency IDentification (RFID) technology. It is assumed that the robot has an odometry system (e.g., wheel encoders) and an RFID reader which provides it with phase-shift measurements of the RFID signal backscattered by the tagged object. Since phases are ambiguous measurements of the tag-reader distance, a Multi-Hypothesis Extended Kalman Filter is proposed to on-line extract, by fusing phase measurements with odometry readings, the range and the bearing of the unknown tag. Once the relative position of the tag is known, a control algorithm is applied to drive the robot toward the desired destination. Numerical results are reported to illustrate the approach.

An RFID-Based Mobile Robot Localization Method Combining Phase Difference and Readability

A novel radio frequency identification (RFID)-based mobile robot global localization method combining two kinds of RFID signal information, i.e., phase difference and readability, is proposed. Specifically, a phase difference model and a classification logic strategy based on readability are built and integrated into a particle filter localization algorithm. Compared with existing RFID localization methods, the proposed localization method can achieve competitive localization performance in an environment with a relatively sparse reference tag distribution and without the need for offline phase drift calibration. A series of real experimental tests were performed, and the results show that the proposed method can localize a mobile robot with centimeter-level position accuracy and satisfactory attitude angle accuracy when the distance between adjacent reference tags is approximately 60 cm, even if all RFID devices are commercial off-the-shelf (COTS). The proposed method provides a promising option for mobile robot localization applications, such as path tracking of mobile robots. Note to Practitioners —Mobile robot localization is a key technology for its location-based services. Considering that radio frequency identification (RFID) is entirely unaffected by light interference and has a globally unique ID, RFID has been regarded as a localization sensor with broad application prospects. This article proposes an RFID-based mobile robot global localization method combining phase difference and readability, by which the mobile robot can be accurately localized in an environment with a relatively sparse reference tag distribution and without the need for offline phase drift calibration. The experimental results indicate that the proposed method can localize the mobile robot with good performance, including centimeter-level position accuracy and satisfactory attitude angle accuracy. The proposed method can effectively contribute to many practical applications, such as the path tracking of a mobile robot.

Pallet-level Classification Using Principal Component Analysis in Ensemble Learning Model

In this paper, we present a machine learning pipeline to solve a multiclass classification of radio frequency identification (RFID) signal strength. The goal is to identify ten pallet levels using nine statistical features derived from RFID signals and four various ensemble learning classification models. The efficacy of the models was evaluated by considering features that were dimensionally reduced via Principal Component Analysis (PCA) and original features. It was shown that the PCA reduced features could provide a better classification accuracy of the pallet levels in comparison to the selection of all features via Extra Tree and Random Forest models.

An Innovative Approach to Track Moving Object based on RFID and Laser Ranging Information

RFID (Radio Frequency Identification) identifies a specific object by radio signals. As the tag provides a unique ID for the purpose of identification, RFID technology effectively solves the ambiguity and occlusion problem that challenges the laser or camera-based approach. This paper proposes an approach to track a moving object based on the integration of RFID and laser ranging information using a particle filter. To be precise, we split laser scan points into different clusters which contain the potential moving objects and calculate the radial velocity of each cluster. The velocity information is compared with the radial velocity estimated from RFID phase difference. In order to achieve the positioning of the moving object, we select a number of best matching clusters to update the weights of the particle filter. To further improve the positioning accuracy, we incorporate RFID signal strength information into the particle filter using a pre-trained sensor model. The proposed approach is tested on a SCITOS service robot under different types of tags and various human velocities. The results show that fusion of signal strength and laser ranging information has significantly increased the positioning accuracy when compared to radial velocity matching-based or signal strength-based approaches. The proposed approach provides a solution for human machine interaction and object tracking, which has potential applications in many fields for example supermarkets, libraries, shopping malls, and exhibitions.

Monitoring of the vacuum infusion process by integrated RFID transponder

To ensure the competitiveness of Germany in the manufacturing of composite parts, greater resource efficiency and higher standards of quality are needed. The process of vacuum infusion is used by many companies to produce a great variety of composite parts for markets like shipbuilding, wind energy and many more. Often, those parts have a complex structure requiring a great amount of skill and experience to produce at a high quality. Therefore, the development process often requires several experimental trials before sufficient quality is reached, using up material and time. By using RFID (radio frequency identification) transponders as sensors, the manufacturing process can be monitored, and less reject is produced. When using RFID transponders as sensors, they can then be used to track the component through further assembly steps and as a storage device during the lifetime of the finished product, enabling a simultaneous way of protecting against product piracy and structural health monitoring.The correlation between the RFID signal and the resin state in the fiber layup is shown for several systems. The results show that RFID transponders can be used for both glass and carbon fiber parts to monitor the production process. This will allow a more efficient production of composite parts, shortening process duration and saving resources. The monitoring of the resin flow and cure in the fiber layers underneath an RFID transponder allows the manufacturer to gain information about the state of the process during production. Resin profiles in a thick fiber layup were successfully tracked using an RFID transponder placed above them. The monitoring of the resin in the fiber layers allows the manufacturer to gain information about the state of the process during production.Summarizing the results of our research, the use of RFID transponders as sensors allows an efficient and process friendly monitoring of the resin behavior during vacuum infusion, and the transponders can be further used in logistics. The tracking of the resin behavior beneath a sensor opens up possibilities for further optimization of the part quality.

An Adaptive Search Algorithm for Detecting Respiratory Artifacts Using a Wireless Passive Wearable Device.

With the use of a wireless, wearable, passive knitted smart fabric device as a strain gauge sensor, the proposed algorithm can estimate biomedical feedback such as respiratory activity. Variations in physical properties of Radio Frequency Identification (RFID) signals can be used to wirelessly detect physiological processes and states. However, it is typical for ambient noise artifacts to appear in the RFID signal making it difficult to identify physiological processes. This paper introduces a new technique for finding these repetitive physiological signals and identifying them into two states, active and inactive, using k-means clustering. The algorithm detects these biomedical events without the need to completely remove the noise components using a semi-unsupervised approach, and with these results, predict the next biomedical event using these classification results. This approach enables real-time noninvasive monitoring for use with actuating medical devices for therapy. Using this approach, the algorithm predicts the onset of respiratory activity in a simulated environment within approximately one second.

Low-Frequency RFID Signal and Power Transfer Circuitry for Capacitive and Resistive Mixed Sensor Array

This paper presents a contactless measurement system for a mixed array of resistive and capacitive sensors exploiting a low-frequency radio-frequency identification (RFID)-based approach. The system is composed of a reader unit which provides power to and exchanges measurement data with a battery-less sensor unit. The sensor unit is based on a transponder operating at 134.2 kHz and a microcontroller. The microcontroller sequentially measures the elements of the sensor array composed of n capacitive and m resistive sensors which share a common terminal. The adopted technique measures the charging time of a resistor–capacitor (RC) circuit, where the resistor or the capacitor can be either the sensing element or a reference component. With the proposed approach, the measured values of the resistive or capacitive elements of the sensor array are first-order independent from the supply voltage level. A prototype has been developed and experimentally tested with resistive elements in the range 400 kΩ–1.2 MΩ and capacitive elements in the range 200 pF–1.2 nF showing measurement resolution values of 1 kΩ and 5 pF, respectively. Operative distances up to 3 cm have been achieved, with readings taken faster than one element of the array per second.

A Review of Passive Radio Frequency Identification Systems for Animal Monitoring in Livestock Facilities

Abstract. Animal facilities are increasing in size, while the availability of skilled workers is decreasing, thus, making it difficult for the farm laborers to ensure the health and well-being of all animals under their care. Passive Radio Frequency Identification (RFID) systems have been successfully used in animal facilities and research has identified potential applications in behavior monitoring for automated illness detection. While RFID signals range in frequency from 9 kHz to 5.8 GHz, the three most common frequencies are Low Frequency (LF, 125 kHz or 134.2 kHz), High Frequency (HF, 13.56 MHz), and Ultra-High Frequency (UHF, 865-868 MHz or 902-928 MHz). The objective of this article is to compare and evaluate the application of these three different RFID systems within large research facilities for livestock and poultry in terms of hardware characteristics, system design, and data processing and usage. Differences in tag construction, availability and cost are evident, but also basic differences in reader and antenna function, such as physics of communication, speed of detection, and anti-collision procedures exist. The systems have significant differences in reading ranges and are known to have varying influence of materials, especially water and metal, on the performance of the systems. However, the data streams, as well as methods of data processing and the creation of events (e.g., visits to a feeder), are similar for all systems. The characteristics mentioned do not necessarily identify an ideal RFID technology but reveal positive and negative aspects of each system. The three different RFID systems have been successfully applied in livestock and poultry facilities. Current research is focused on the utilization of the RFID data in prediction and decision models for illness, animal welfare, and management actions. Keywords: Behavior, Cattle, Frequency ranges, Health and welfare, Poultry, Swine, Transponder.

RFID Technology for Management and Tracking: e-Health Applications.

Radio frequency identification (RFID) has become a key technology in the logistics and management industry, thanks to distinctive features such as the low cost of RFID tags, and the easiness of the RFID tags’ deployment and integration within the items to be tracked. In consequence, RFID plays a fundamental role in the so-called digital factory or 4.0 Industry, aiming to increase the level of automatization of industrial processes. In addition, RFID has also been found to be of great help in improving the tracking of patients, medicines, and medical assets in hospitals, where the digitalization of these operations improves their efficiency and safety. This contribution reviews the state-of-the-art of RFID for e-Health applications, describing the contributions to improve medical services and discussing the limitations. In particular, it has been found that a lot of effort has been put into software development, but in most of the cases a detailed study of the physical layer (that is, the characterization of the RFID signals within the area where the system is deployed) is not properly conducted. This contribution describes a basic RFID system for tracking and managing assets in hospitals, aiming to provide additional details about implementation aspects that must be considered to ensure proper functionality of the system. Although the scope of the RFID system described in this contribution is restricted to a small area of the hospital, the architecture is fully scalable to cover the needs of the different medical services in the hospital. Ultra high-frequency (UHF) RFID technology is selected over the most extended near-field communication (NFC) and high-frequency (HF) RFID technology to minimize hardware infrastructure. In particular, UHF RFID also makes the coverage/reading area conformation easier by using different kinds of antennas. Information is stored in a database, which is accessed from end-user mobile devices (tablets, smartphones) where the position and status of the assets to be tracked are displayed.